Simplified README index.

1 files changed, 7 insertions, 7 deletions

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 <a name="nn.dok"/>
 # Neural Network Package #
 
-This package provides an easy way to build and train simple or complex
-neural networks. The documentation is divided into different sections:
+This package provides an easy way to build and train simple or complex neural networks:
  * Modules are the bricks used to build neural networks. Each are themselves neural networks, but can be combined with other networks using containers to create complex neural networks:
    * [Module](doc/module.md#nn.Module) : abstract class inherited by all modules;
    * [Containers](doc/containers.md#nn.Containers) : container classes like [Sequential](doc/containers.md#nn.Sequential), [Parallel](doc/containers.md#nn.Parallel) and [Concat](doc/containers.md#nn.Concat);
    * [Transfer functions](doc/transfer.md#nn.transfer.dok) : non-linear functions like [Tanh](doc/transfer.md#nn.Tanh) and [Sigmoid](doc/transfer.md#nn.Sigmoid);
-   * [Simple layers](doc/simple.md#nn.simplelayers.dok) : like [Linear](doc/simple.md#nn.Linear), [Mean](doc/simple.md#nn.Mean), [Max](doc/simple.md#nn.Max) and [Reshape](doc/simple.md#nn.Reshape); and
+   * [Simple layers](doc/simple.md#nn.simplelayers.dok) : like [Linear](doc/simple.md#nn.Linear), [Mean](doc/simple.md#nn.Mean), [Max](doc/simple.md#nn.Max) and [Reshape](doc/simple.md#nn.Reshape); 
    * [Convolution layers](doc/convolution.md#nn.convlayers.dok) : [Temporal](doc/convolution.md#nn.TemporalModules),  [Spatial](doc/convolution.md#nn.SpatialModules) and [Volumetric](doc/convolution.md#nn.VolumetricModules) convolutions ; 
- * [Criterions](doc/criterion.md#nn.Criterions) compute a gradient according to a given loss function given an input and a target. Common criterions are :
-   * [MSECriterion](doc/criterion.md#nn.MSECriterion) : the Mean Squared Error criterion used for regression; and 
-   * [ClassNLLCriterion](doc/criterion.md#nn.ClassNLLCriterion) : the Negative Log Likelihood (cross-entropy) criterion used for classification;
+ * Criterions compute a gradient according to a given loss function given an input and a target:
+   * [Criterions](doc/criterion.md#nn.Criterions) : a list of all criterions, including [Criterion](doc/criterion.md#nn.Criterion), the abstract class;
+   * [MSECriterion](doc/criterion.md#nn.MSECriterion) : the Mean Squared Error criterion used for regression; 
+   * [ClassNLLCriterion](doc/criterion.md#nn.ClassNLLCriterion) : the Negative Log Likelihood criterion used for classification;
  * Additional documentation :
    * [Overview](doc/overview.md#nn.overview.dok) of the package essentials including modules, containers and training;
-   * [Training](doc/training.md#nn.traningneuralnet.dok) : how to a neural network using [StochasticGradient](doc/training.md#nn.StochasticGradient) or with with [your own loop](doc/training.md#nn.DoItYourself) ; and
+   * [Training](doc/training.md#nn.traningneuralnet.dok) : how to train a neural network using [StochasticGradient](doc/training.md#nn.StochasticGradient);
    * [Testing](doc/testing.md) : how to test your modules.